






"""simple docstring"""







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available





class 	UpperCamelCase_			(							a_	):
  _A							:       List[str]					=						['image_processor', 'tokenizer']
  _A							:       Any					=						'OwlViTImageProcessor'
  _A							:       Tuple					=						('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(   self			,		snake_case__=None			,		snake_case__=None			,		**snake_case__					)      ->      int:



         """simple docstring"""





         UpperCAmelCase						     =      None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead."""			,		snake_case__			,		)
                UpperCAmelCase						     =      kwargs.pop("""feature_extractor"""					)

         UpperCAmelCase						     =      image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""					)
         if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""					)

         super().__init__(snake_case__			,		snake_case__					)



  def __call__(   self			,		snake_case__=None			,		snake_case__=None			,		snake_case__=None			,		snake_case__="max_length"			,		snake_case__="np"			,		**snake_case__					)      ->      Union[str, Any]:



         """simple docstring"""





         if text is None and query_images is None and images is None:
                raise ValueError(
                    """You have to specify at least one text or query image or image. All three cannot be none."""					)

         if text is not None:
                if isinstance(snake_case__			,		snake_case__					) or (isinstance(snake_case__			,		snake_case__					) and not isinstance(text[0]			,		snake_case__					)):
                       UpperCAmelCase						     =      [self.tokenizer(snake_case__			,		padding=snake_case__			,		return_tensors=snake_case__			,		**snake_case__					)]

                elif isinstance(snake_case__			,		snake_case__					) and isinstance(text[0]			,		snake_case__					):
                       UpperCAmelCase						     =      []

                       # Maximum number of queries across batch
                       UpperCAmelCase						     =      max([len(snake_case__					) for t in text]					)

                       # Pad all batch samples to max number of text queries
                       for t in text:
                              if len(snake_case__					) != max_num_queries:
                                     UpperCAmelCase						     =      t + [""" """] * (max_num_queries - len(snake_case__					))

                              UpperCAmelCase						     =      self.tokenizer(snake_case__			,		padding=snake_case__			,		return_tensors=snake_case__			,		**snake_case__					)
                              encodings.append(snake_case__					)
                else:
                       raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""					)

                if return_tensors == "np":
                       UpperCAmelCase						     =      np.concatenate([encoding["""input_ids"""] for encoding in encodings]			,		axis=0					)
                       UpperCAmelCase						     =      np.concatenate([encoding["""attention_mask"""] for encoding in encodings]			,		axis=0					)

                elif return_tensors == "jax" and is_flax_available():
                       import jax.numpy as jnp

                       UpperCAmelCase						     =      jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]			,		axis=0					)
                       UpperCAmelCase						     =      jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]			,		axis=0					)

                elif return_tensors == "pt" and is_torch_available():
                       import torch

                       UpperCAmelCase						     =      torch.cat([encoding["""input_ids"""] for encoding in encodings]			,		dim=0					)
                       UpperCAmelCase						     =      torch.cat([encoding["""attention_mask"""] for encoding in encodings]			,		dim=0					)

                elif return_tensors == "tf" and is_tf_available():
                       import tensorflow as tf

                       UpperCAmelCase						     =      tf.stack([encoding["""input_ids"""] for encoding in encodings]			,		axis=0					)
                       UpperCAmelCase						     =      tf.stack([encoding["""attention_mask"""] for encoding in encodings]			,		axis=0					)

                else:
                       raise ValueError("""Target return tensor type could not be returned"""					)

                UpperCAmelCase						     =      BatchEncoding()
                UpperCAmelCase						     =      input_ids
                UpperCAmelCase						     =      attention_mask

         if query_images is not None:
                UpperCAmelCase						     =      BatchEncoding()
                UpperCAmelCase						     =      self.image_processor(
                    snake_case__			,		return_tensors=snake_case__			,		**snake_case__					).pixel_values
                UpperCAmelCase						     =      query_pixel_values

         if images is not None:
                UpperCAmelCase						     =      self.image_processor(snake_case__			,		return_tensors=snake_case__			,		**snake_case__					)

         if text is not None and images is not None:
                UpperCAmelCase						     =      image_features.pixel_values
                return encoding
         elif query_images is not None and images is not None:
                UpperCAmelCase						     =      image_features.pixel_values
                return encoding
         elif text is not None or query_images is not None:
                return encoding
         else:
                return BatchEncoding(data=dict(**snake_case__					)			,		tensor_type=snake_case__					)



  def 		UpperCamelCase_					(   self			,		*snake_case__			,		**snake_case__					)      ->      List[str]:



         """simple docstring"""





         return self.image_processor.post_process(*snake_case__			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		*snake_case__			,		**snake_case__					)      ->      Optional[int]:



         """simple docstring"""





         return self.image_processor.post_process_object_detection(*snake_case__			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		*snake_case__			,		**snake_case__					)      ->      int:



         """simple docstring"""





         return self.image_processor.post_process_image_guided_detection(*snake_case__			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		*snake_case__			,		**snake_case__					)      ->      Any:



         """simple docstring"""





         return self.tokenizer.batch_decode(*snake_case__			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		*snake_case__			,		**snake_case__					)      ->      Dict:



         """simple docstring"""





         return self.tokenizer.decode(*snake_case__			,		**snake_case__					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      Union[str, Any]:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,		snake_case__			,		)
         return self.image_processor_class



  @property
  def 		UpperCamelCase_					(   self					)      ->      List[str]:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,		snake_case__			,		)
         return self.image_processor






'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



